
IBM2015: The original corpus is organized into two splits: 19 topics are used as a held-out set for model selection and hyperparameter tuning, whereas the remaining 39 topics are used for the final training and evaluation. We adopt a 5-fold cross-validation on the latter set of 39 topics, while we calibrate the hyper-parameters of our architecture using the available held-out set.

UKP-Sent: We consider the same leave-one-out (LOO) test, repeated 10 times, as in [1]}. We do not compare with strong LSTM-based and BERT-based baselines as reported in [1]} due to the different training setup. However, in early experiments with the same classification task as in [1]} the performance of GNN models nearly matched that of LSTM-BERT and BERT-base.

PE: The original corpus is organized into two splits: 322 essays for training, and 80 for test. We performed a 5-fold cross-validation on the training data for model selection, and we finally evaluate performance on the test set. Due to the limited size of the corpus, for neural architectures we also employ a multi-start procedure, choosing the best model on the validation set via early stopping.

AbstRCT: We follow the same evaluation methodology as in [4]}, where a repeated train-and-test procedure is considered both for tuning and testing. In addition to the neural baselines introduced for the other corpora, we consider state-of-the-art models for this task as reported in [4]}. In particular, these baselines are complex neural architectures mainly based on residual networks and the attention mechanism [6]}.

